Apparently, with the guidance of a more accurate JND map, the JND-contaminated image (obtained by Eq. (12)) with same noise level should have better visual quality. As stated, for different JND models, we can adjust the value of \(\theta \)  in Eq. (12) to ensure that almost the same amount of noise is injected. Then, the performance of a JND model can be evaluated by measuring the MS-SSIM [1]} score and the mean opinion score (MOS) of the obtained noise-contaminated image. MS-SSIM is a popular full-reference image quality metric that is measured between the original image and the contaminated image. Theoretically, the MS-SSIM score falls in the range \([0,1]\)  and a higher MS-SSIM score indicates better visual quality. The MOS score is obtained via subjective assessment experiments. Another 30 subjects are invited to participate the subjective experiments. Each subject is asked to assign an opinion score in the range \([-1,0]\)  to a specific image pair including one original image and one JND-contaminated image. Note that the two images are juxtaposed a 65-in monitor at left and right with random order for comparison. A score equals to 0 means the visual quality of the JND-contaminated image is equal to that of the original image while a score equal to -1 means the visual quality of the JND contaminated image is dramatically worse than that of the original image. As a result, for each contaminated image, 30 scores are collected from all subjects. After removing the outlier, the mean value of the retained opinion scores is calculated as the MOS. A more accurate JND model will result in higher MS-SSIM score and MOS than the competitors while their PSNR values are almost the same (the same noise level).
